Compressive nonparametric graphical model selection for time series
dc.contributor.author
Jung, Alexander
dc.contributor.author
Heckel, Reinhard
dc.contributor.author
Bölcskei, Helmut
dc.contributor.author
Hlawatsch, Franz
dc.date.accessioned
2017-06-11T13:33:31Z
dc.date.available
2017-06-11T13:33:31Z
dc.date.issued
2014
dc.identifier.isbn
978-1-4799-2893-4
dc.identifier.isbn
978-1-4799-2894-1
dc.identifier.isbn
978-1-4799-2892-7
dc.identifier.other
10.1109/ICASSP.2014.6853700
dc.identifier.uri
http://hdl.handle.net/20.500.11850/91924
dc.language.iso
en
dc.publisher
IEEE
dc.subject
Sparsity
dc.subject
Graphical model selection
dc.subject
Multitask learning
dc.subject
Nonparametric time series
dc.subject
LASSO
dc.title
Compressive nonparametric graphical model selection for time series
dc.type
Conference Paper
ethz.book.title
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP): May 4, 2014 - May 9, 2014, Florence, Italy
ethz.pages.start
769
ethz.pages.end
773
ethz.event
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014)
ethz.event.location
Florence, Italy
ethz.event.date
May 4-9, 2014
ethz.publication.place
Piscataway, NJ
ethz.publication.status
published
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::03610 - Boelcskei, Helmut / Boelcskei, Helmut
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::03610 - Boelcskei, Helmut / Boelcskei, Helmut
ethz.date.deposited
2017-06-11T13:34:13Z
ethz.source
ECIT
ethz.identifier.importid
imp5936527dbb70381064
ethz.ecitpid
pub:144619
ethz.eth
yes
ethz.availability
Metadata only
ethz.rosetta.installDate
2017-07-20T16:18:52Z
ethz.rosetta.lastUpdated
2020-02-14T14:24:38Z
ethz.rosetta.versionExported
true
ethz.COinS
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Publikationstyp
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Conference Paper [33071]